Tarrant County Redistricting, 2020

Tarrant County District Boundaries

All shapefiles were projected into the Texas mapping projection for the purposes of creating maps and calculating the physical properties of the districts’ shape.

Map 1: Reference Map of Commissioners Districts

Map 2: Reference Map of JP & Constable Districts

## GEOPROCESSING OF OTHER SPATIAL FEATURES FOR MAPS 
    #If you need other spatial features for the maps process/clean in this chunk
schools_prj <- schools %>% 
  st_transform(st_crs(tx_precincts))

## COMMISH INTERACTIVE MAPPING
commish_pal <- colorFactor(
  palette = "Spectral",
  domain = cnty_commish_dists_prj$district_type
)

leaflet() %>%
  addTiles() %>% 
  addPolygons(
    data = cnty_commish_dists_prj %>% st_transform(prj_webmap),
    fillColor = ~commish_pal(district_type),
    color = "white",
    opacity = 0.5,
    fillOpacity = 0.5,
    weight = 1) %>% 
  addLegend(
    data = cnty_commish_dists_prj,
    pal = commish_pal,
    values = ~district_type,
    title = "Map 3: County Commissioner Interactive Map"
  )

## JP/CONSTABLE INTERACTIVE MAPPING
JP_pal <- colorFactor(
  palette = "Accent",
  domain = cnty_JP_dists_prj$district_type
)

leaflet() %>%
  addTiles() %>% 
  addPolygons(
    data = cnty_JP_dists_prj %>% st_transform(prj_webmap),
    fillColor = ~JP_pal(district_type),
    color = "white",
    opacity = 0.5,
    fillOpacity = 0.5,
    weight = 1) %>% 
  addLegend(
    data = cnty_JP_dists_prj,
    pal = JP_pal,
    values = ~district_type,
    title = "Map 4: JP/Constable Districts Interactive Map"
  )

Properties of the District’s Boundaries

The area and perimeter of each district was calculated. Then, several compactness measures were computed. For all four measures, a score closer to 1 indicates higher compactness.

Among the county commissioners’ districts, district 1 stands out as the least compact (i.e., most irregular). District 1 scores higher on convexity than all of the other measures, which indicates that the district has a convex shape. However, the elongated maximum width of the district and its area set it apart as particularly irregular.

Similarly, the JP districts tend to be more convex in shape. However, a handful score high on elongation, like JP districts 2, 5, and 8.

##### CALCULATE COMPACTNESS MEASURES
polsby_popper = function(poly1) {
  return(drop_units(
    4 * pi * st_area(poly1) / st_length(st_boundary(poly1))^2))
}

schwartzberg = function(poly1) {
  return(polsby_popper(poly1)^-0.5)
}

inv_schwartzberg = function(poly1) {
  return(polsby_popper(poly1)^0.5)
}

area_convex_hull = function(poly1, ch = NULL) {
  if (is.null(ch)) {
    ch = st_convex_hull(st_geometry(poly1))
  }
  return(drop_units(st_area(poly1) / st_area(ch)))
}

reock = function(poly1, mbc = NULL) {
  if (is.null(mbc)) {
    mbc = st_minimum_bounding_circle(st_convex_hull(st_geometry(poly1)))
  }
  return(drop_units(st_area(poly1) / st_area(mbc)))
}

cnty_districts_land_table <- rbind(cnty_commish_dists_prj, cnty_JP_dists_prj) %>%
  mutate(polsby_popper = polsby_popper(geometry),
         schwartzberg = schwartzberg(geometry),
         inv_schwartzberg = inv_schwartzberg(geometry),
         reock = reock(geometry),
         area_convex_hull = area_convex_hull(geometry))

knitr::kable(cnty_districts_land_table %>%
               st_set_geometry(NULL) %>%
               select(-schwartzberg) %>%
               rename(`District Type` = "district_type",
                       Precincts = n_precincts,
                      `polsby-popper` = "polsby_popper",
                      `area convex hull` = "area_convex_hull",
                      `inverse schwartzberg` = "inv_schwartzberg"
             ), caption = "Table 1: Compactness Scores by District")

Table 1: Compactness Scores by District

District Type	Precincts	area	perimeter	polsby-popper	inverse schwartzberg	reock	area convex hull
CC-01	219	686835752 [m^2]	351645.55 [m]	0.1187862	0.3446537	0.2618557	0.6353092
CC-02	167	412429004 [m^2]	183569.10 [m]	0.4242672	0.6513580	0.6071442	0.9286122
CC-03	159	548775689 [m^2]	287734.99 [m]	0.3608833	0.6007356	0.4479380	0.8566063
CC-04	184	685991592 [m^2]	319305.49 [m]	0.1755394	0.4189743	0.5030534	0.7731104
JP-01	104	224869063 [m^2]	139393.51 [m]	0.3288311	0.5734380	0.4212736	0.8000391
JP-02	85	179082236 [m^2]	95551.95 [m]	0.2573010	0.5072485	0.3257384	0.7368410
JP-03	116	424499209 [m^2]	213865.30 [m]	0.4688542	0.6847293	0.4940934	0.8726436
JP-04	69	497307299 [m^2]	186897.24 [m]	0.5180230	0.7197381	0.5399593	0.8862616
JP-05	85	101956693 [m^2]	93082.77 [m]	0.1845501	0.4295929	0.3216107	0.6279513
JP-06	78	377705747 [m^2]	171786.65 [m]	0.4831084	0.6950600	0.5184064	0.8955504
JP-07	107	363756270 [m^2]	177608.86 [m]	0.4328215	0.6578917	0.3817434	0.8646804
JP-08	85	164856067 [m^2]	95705.57 [m]	0.2565861	0.5065433	0.3433722	0.7580357

Population Characteristics

Table 1, shown below, summarizes the growth in Tarrant County from the last decennial Census in 2010 through 2019. Tarrant has added just over 240,000 people, which is a 13% change from 2010. The citizen-voting age population has grown by just under 25,000 - a 2% growth rate.

## 2010
census2010_table <- readRDS("census2010_table.rds")

## 2019
acs19_full <- readRDS("acs19_full.rds")

## Time Table
knitr::kable(acs19_full %>%
  summarize(total_population_2019 = sum(pop, na.rm =T),
            cvap_2019 = sum(CVAP, na.rm =T)) %>%
  pivot_longer(ends_with("_2019"),
    names_to = "variable",
    values_to = "Total, 2019") %>% 
  mutate(variable = str_remove(variable, "_2019"),
         variable = str_replace_all(variable, "_", " ")) %>%
  left_join(census2010_table, by = c("variable")) %>%
  relocate(variable, `Total, 2010`, `Total, 2019`) %>%
  mutate(`Total Change` = `Total, 2019` - `Total, 2010`,
         `% Change` = `Total Change` / `Total, 2010`,
         `% Change` =  percent(`% Change`,
                               accuracy = 0.01,
                               scale = 100,
                               label = "%"),
         `Total Change` = format(`Total Change`, big.mark   = ","),
         `Total, 2010` = format(`Total, 2010`, big.mark   = ","),
         `Total, 2019` = format(`Total, 2019`, big.mark = ",")), 
  caption = "Table 2: Tarrant County Population 2010 & 2019")

Table 2: Tarrant County Population 2010 & 2019

variable	Total, 2010	Total, 2019	Total Change	% Change
total population	1,809,034	2,049,770	240,736	13.31%
cvap	1,301,973	1,326,447	24,474	1.88%

## CVAP 2019 Summary
CVAP19_summary <- acs19_full %>%
  select(c(3:19)) %>%
  select(c(-6:-7, -9:-12)) %>%
  summarise_if(is.numeric, sum, na.rm = T) %>%
  relocate(`High school graduate (includes equivalency)`,
           .after = `No high school diploma`) %>%
  pivot_longer(cols = c(1:11),
               names_to = "Group", 
               values_to = "Total") %>%
  mutate(`% of CVAP` = percent(Total / first(Total),
                           accuracy = 0.01,
                           scale = 100,
                           label = "%" ),
         Total = format(Total, big.mark = ","))

knitr::kable(CVAP19_summary,
            caption = "Table 2.1: Summary of Citizen Voting-Age Demographics")

Table 2.1: Summary of Citizen Voting-Age Demographics

Group	Total	% of CVAP
CVAP	1,326,447	100.00%
18 to 29 years	305,006	22.99%
30 to 44 years	356,637	26.89%
45 to 64 years	449,268	33.87%
65 years and over	215,536	16.25%
Income in the past 12 months below poverty level	116,083	8.75%
Income in the past 12 months at or above the poverty level	1,190,962	89.79%
No high school diploma	127,341	9.60%
High school graduate (includes equivalency)	335,023	25.26%
Some college or Associates	449,649	33.90%
Bachelor’s or Graduate degree	414,434	31.24%

## Race 2019 Summary
Race19_summary <- acs19_full %>%
  select(c(2, 20:27)) %>%
  rename(`Total population` = "pop") %>%
  summarise_if(is.numeric, sum, na.rm = T) %>%
  pivot_longer(cols = c(1:9),
               names_to = "Group", 
               values_to = "Total") %>%
  mutate(`% of Population` = percent(Total / first(Total),
                           accuracy = 0.01,
                           scale = 100,
                           label = "%" ),
         Total = format(Total, big.mark = ","),
         Group = str_replace(Group, "_", " "))

knitr::kable(Race19_summary,
            caption = "Table 2.3: Summary of Racial Demographics in Tarrant County")

Table 2.3: Summary of Racial Demographics in Tarrant County

Group	Total	% of Population
Total population	2,049,770	100.00%
White, Not Latinx	957,676	46.72%
Black, Not Latinx	330,853	16.14%
Two or More, Not Latinx	1,330	0.06%
Other, Not Latinx	4,441	0.22%
NHPI, Not Latinx	3,802	0.19%
Asian, Not Latinx	110,144	5.37%
American Indian, Not Latinx	6,154	0.30%
Any race, Latinx	590,485	28.81%

## Household 2019 Summary
HH19_summary <- acs19_full %>%
  select(c(28:45)) %>%
  relocate(limited_english, .after = Households) %>%
  rename(`Limited English Proficiency` = "limited_english") %>%
  summarise_if(is.numeric, sum, na.rm = T) %>%
  pivot_longer(cols = c(1:18),
               names_to = "Group", 
               values_to = "Total") %>%
  mutate(`% of Population` = percent(Total / first(Total),
                           accuracy = 0.01,
                           scale = 100,
                           label = "%" ),
         Total = format(Total, big.mark = ","),
         Group = str_replace(Group, "_", " "))

knitr::kable(HH19_summary,
            caption = "Table 2.4: Summary of Household Demographics in Tarrant County")

Table 2.4: Summary of Household Demographics in Tarrant County

Group	Total	% of Population
Households	708,252	100.00%
Limited English Proficiency	44,711	6.31%
Less than $10,000	32,868	4.64%
$10,000 to $14,999	23,111	3.26%
$15,000 to $19,999	24,250	3.42%
$20,000 to $24,999	26,811	3.79%
$25,000 to $29,999	29,477	4.16%
$30,000 to $34,999	32,679	4.61%
$35,000 to $39,999	31,016	4.38%
$40,000 to $44,999	29,985	4.23%
$45,000 to $49,999	25,389	3.58%
$50,000 to $59,999	57,163	8.07%
$60,000 to $74,999	73,924	10.44%
$75,000 to $99,999	96,569	13.63%
$100,000 to $124,999	68,829	9.72%
$125,000 to $149,999	47,431	6.70%
$150,000 to $199,999	52,068	7.35%
$200,000 or more	56,682	8.00%

#### GEOPROCESSING OF BLOCK GROUPS
cnty_bgs_prj <- county_bgs %>%
  dplyr::select(GEOID) %>%
  st_transform(st_crs(cnty_commish_dists_prj)) %>%
  mutate(bg_area = st_area(.)) %>%
  left_join(acs19_full, by = c("GEOID" = "cbg")) %>%
  dplyr::select(-GEOID)

  
#### APPORTION DEMOGRAPHIC POPULATIONS TO DISTRICTS
cnty_commish_apportion <- st_interpolate_aw(cnty_bgs_prj,
                                           cnty_commish_dists_prj,
                                           extensive = T)

cnty_JP_apportion <- st_interpolate_aw(cnty_bgs_prj,
                                       cnty_JP_dists_prj,
                                       extensive = T)

Table 2.5: County Commissioner’s CVAP Apportionment

  # CVAP apportionment - CC
cnty_commish_apportion_cvap <- cnty_commish_apportion %>%
  dplyr::select(Group.1, pop:X65.years.and.over,
                High.school.graduate..includes.equivalency.,
                Income.in.the.past.12.months.below.poverty.level:Bachelor.s.or.Graduate.degree) %>%
  relocate(No.high.school.diploma, .after = X65.years.and.over) %>%
  relocate(Some.college.or.Associates, .after = High.school.graduate..includes.equivalency.) %>%
  relocate(Bachelor.s.or.Graduate.degree, .after=Some.college.or.Associates) %>%
  rename(District = "Group.1",
         total_population = "pop") %>%
  st_set_geometry(NULL) %>%
  pivot_longer(cols = c(2:13),
               names_to = "Group",
               values_to = "estimated_count") %>%
  mutate(Group = str_remove(Group, "X"),
         Group = str_replace_all(Group, "/.", " "),
         District = paste0("CC-0", District)) %>%
  pivot_wider(id_cols = District,
              names_from = "Group",
              values_from = "estimated_count") %>%
  mutate_each(funs(prettyNum(.,  big.mark=",")))  


rmarkdown::paged_table(cnty_commish_apportion_cvap)

Table 2.6: Apportionment of Race by Commissioner’s District

 # race apportionment - CC
cnty_commish_apportion_race <- cnty_commish_apportion %>%
  dplyr::select(Group.1, pop, White..Not_Latinx:Any.race..Latinx) %>%
  rename(District = "Group.1",
         total_population = "pop") %>%
  st_set_geometry(NULL) %>%
  pivot_longer(cols = c(total_population:Any.race..Latinx),
               names_to = "Group",
               values_to = "estimated_count") %>%
  mutate(Group = str_remove(Group, "X"),
         Group = str_replace_all(Group, "/.", " "),
         District = paste0("CC-0", District)) %>%
  pivot_wider(id_cols = District,
              names_from = "Group",
              values_from = "estimated_count") %>%
  mutate_each(funs(prettyNum(.,  big.mark=",")))  


rmarkdown::paged_table(cnty_commish_apportion_race,list(rows.print = 4))

Table 2.7: JP/Constable CVAP Apportionment

  # CVAP apportionment - JP
cnty_JP_apportion_cvap <- cnty_JP_apportion %>%
  dplyr::select(Group.1, pop:X65.years.and.over,
                High.school.graduate..includes.equivalency.,
                Income.in.the.past.12.months.below.poverty.level:Bachelor.s.or.Graduate.degree) %>%
  relocate(No.high.school.diploma, .after = X65.years.and.over) %>%
  relocate(Some.college.or.Associates, .after = High.school.graduate..includes.equivalency.) %>%
  relocate(Bachelor.s.or.Graduate.degree, .after=Some.college.or.Associates) %>%
  rename(District = "Group.1",
         total_population = "pop") %>%
  st_set_geometry(NULL) %>%
  pivot_longer(cols = c(2:13),
               names_to = "Group",
               values_to = "estimated_count") %>%
  mutate(Group = str_remove(Group, "X"),
         Group = str_replace_all(Group, "/.", " "),
         District = paste0("JP-0", District)) %>%
  pivot_wider(id_cols = District,
              names_from = "Group",
              values_from = "estimated_count") %>%
  mutate_each(funs(prettyNum(.,  big.mark=",")))  


rmarkdown::paged_table(cnty_JP_apportion_cvap, list(rows.print = 8))

Table 2.8: Apportionment of Race by JP/Constable District

 # race apportionment - JP
cnty_JP_apportion_race <- cnty_JP_apportion %>%
  dplyr::select(Group.1, pop, White..Not_Latinx:Any.race..Latinx) %>%
  rename(District = "Group.1",
         total_population = "pop") %>%
  st_set_geometry(NULL) %>%
  pivot_longer(cols = c(total_population:Any.race..Latinx),
               names_to = "Group",
               values_to = "estimated_count") %>%
  mutate(Group = str_remove(Group, "X"),
         Group = str_replace_all(Group, "/.", " "),
         District = paste0("JP-0", District)) %>%
  pivot_wider(id_cols = District,
              names_from = "Group",
              values_from = "estimated_count") %>%
  mutate_each(funs(prettyNum(.,  big.mark=",")))  


rmarkdown::paged_table(cnty_JP_apportion_race, list(rows.print = 8, cols.print = 9))

2020 Election Results & Political Partisanship

Election Results 2020

Only two county commissioners seats were up for election in 2020 - seats 1 and 3. The outcomes of the elections are summarized below in Table 2. The Democratic candidate running for seat 1, Roy Brooks, won his seat with 60% of the vote. The race for seat 3 was not a victory for Democrats. Democratic candidate Kathy Braatz took approximately 36% of the votes in district 3. There is not a substantial difference between the mean and median vote share at the precinct level. Thus, there is not strong evidence of variation between precincts in voting patterns in either of these districts.

## COUNTY COMMISSIONER'S RESULTS
commish_2020_summary <- commish_2020_all %>%
  mutate(vote_share_d_prec = (dem_votes/total_votes) * 100) %>%
  group_by(district) %>%
  summarize("Precincts" = n(),
            `D Votes` = sum(dem_votes, na.rm =T),
            `R Votes` = sum(repub_votes, na.rm = T),
            `D Vote Share` = percent(`D Votes`/(`D Votes`+ `R Votes`),
                                   accuracy = 0.01,
                                   scale = 100,
                                   label = "%"),
            `Mean D Vote Share` = round(mean(vote_share_d_prec, na.rm = T), digits=2),
            `Median D Vote Share` = round(median(vote_share_d_prec, na.rm = T), digits=2),
            `Vote Skew` = round(`Mean D Vote Share`-`Median D Vote Share`, digits=2),
            `D Votes` = format(`D Votes`, big.mark = ","),
            `R Votes` = format(`R Votes`, big.mark = ",")
            )

knitr::kable(commish_2020_summary,
             caption = "Table 2: Summary of 2020 County Commissioners Election Results")

Table 2: Summary of 2020 County Commissioners Election Results

district	Precincts	D Votes	R Votes	D Vote Share	Mean D Vote Share	Median D Vote Share	Vote Skew
Commissioner 01	218	105,482	69,223	60.38%	63.28	65.22	-1.94
Commissioner 03	159	87,694	156,123	35.97%	34.87	35.04	-0.17

## CONSTABLE'S RESULTS
constable_2020_summary <- constable_2020_all %>%
  mutate(vote_share_d_prec = (dem_votes/total_votes) * 100) %>%
  group_by(district) %>%
  summarize("Precincts" = n(),
            `D Votes` = sum(dem_votes, na.rm =T),
            `R Votes` = sum(repub_votes, na.rm = T),
            `D Vote Share` = percent(`D Votes`/(`D Votes`+ `R Votes`),
                                   accuracy = 0.01,
                                   scale = 100,
                                   label = "%"),
            `Mean D Vote Share` = round(mean(vote_share_d_prec, na.rm = T), digits=2),
            `Median D Vote Share` = round(median(vote_share_d_prec, na.rm = T), digits=2),
            `Vote Skew` = round(`Mean D Vote Share`-`Median D Vote Share`, digits=2),
            `D Votes` = format(`D Votes`, big.mark = ","),
            `R Votes` = format(`R Votes`, big.mark = ",")
            )

knitr::kable(constable_2020_summary,
             caption = "Table 2: Summary of 2020 Constables Election Results")

Table 2: Summary of 2020 Constables Election Results

district	Precincts	D Votes	R Votes	D Vote Share	Mean D Vote Share	Median D Vote Share	Vote Skew
Constable 01	103	46,362	60,341	43.45%	45.86	42.37	3.49
Constable 02	85	36,060	34,652	51.00%	53.51	54.62	-1.11
Constable 03	116	0	142,646	0.00%	NaN	NA	NaN
Constable 04	69	0	73,928	0.00%	NaN	NA	NaN
Constable 05	85	29,040	0	100.00%	100.00	100.00	0.00
Constable 06	78	50,185	54,743	47.83%	46.81	45.90	0.91
Constable 07	107	77,728	68,218	53.26%	54.62	59.00	-4.38
Constable 08	86	41,259	0	100.00%	100.00	100.00	0.00

Partisanship Summary

Commissioner’s district 1 has the most democratic voters and district 3 has the fewest. These data help make sense of the results from the 2020 election. What’s noteworthy is that district 3 has a noticeably higher number of high propensity voters than low propensity compared to other districts.

The number of registered voters for district 3 is noticeably higher than the other districts. This district may be an appropriate one to shift boundaries to distribute some more voters, particularly high-propensity voters to district 1 or 2.

## PARTSIANSHIP SUMMARY BY COMMISSIONERS
partisanship_commish_summary <- partisanship %>%
  right_join(tx_precincts, by = c("dnc_precinct_id" = "dnc_id")) %>%
  right_join(county_precincts, by = c("PREC" = "Pct_Char")) %>%
  group_by(Commish) %>%
  summarize(`Registered Voters` = format(sum(total_reg_voters, na.rm = T), big.mark=","),
            `Democratic Voters` = format(sum(highd, lowd, na.rm = T), big.mark = ","),
            `High Propensity Dems` = format(sum(highd, na.rm = T), big.mark = ","),
            `Low Propensity Dems` = format(sum(lowd, na.rm = T), big.mark = ","),
             Precincts = n()
            ) %>%
  rename(`County Commissioner` = "Commish")

knitr::kable(partisanship_commish_summary,
             caption = "Table 3: Summary of Partisanship by County Commissioners District")

Table 3: Summary of Partisanship by County Commissioners District

County Commissioner	Registered Voters	Democratic Voters	High Propensity Dems	Low Propensity Dems	Precincts
01	352,578	151,140	68,467	82,673	394
02	373,360	135,768	67,101	68,667	245
03	413,142	82,903	52,589	30,314	220
04	342,664	89,473	44,239	45,234	344

There is a lot variation in the number of registered voters between the 8 JP/constable’s districts. At the low end, district 5 has approximately 80,000 registered voters, and at the upper-end district 3 has approximately 316,000 voters. The JP districts with the most democratic voters are districts: 7, 8, and 3, in descending order. Districts 7 and 3 have the highest number of high propensity democratic voters.

## PARTSIANSHIP SUMMARY BY JP
partisanship_JP_summary <- partisanship %>%
  right_join(tx_precincts, by = c("dnc_precinct_id" = "dnc_id")) %>%
  right_join(county_precincts, by = c("PREC" = "Pct_Char")) %>%
  group_by(JP) %>%
  summarize(`Registered Voters` = format(sum(total_reg_voters, na.rm = T), big.mark=","),
            `Democratic Voters` = format(sum(highd, lowd, na.rm = T), big.mark = ","),
            `High Propensity Dems` = format(sum(highd, na.rm = T), big.mark = ","),
            `Low Propensity Dems` = format(sum(lowd, na.rm = T), big.mark = ","),
             Precincts = n()
            )

knitr::kable(partisanship_JP_summary,
             caption = "Table 4: Summary of Partisanship by JP/Constables District")

Table 4: Summary of Partisanship by JP/Constables District

JP	Registered Voters	Democratic Voters	High Propensity Dems	Low Propensity Dems	Precincts
01	202,187	50,340	25,847	24,493	168
02	146,535	49,816	22,494	27,322	119
03	316,012	66,147	42,445	23,702	155
04	170,687	31,945	17,731	14,214	132
05	80,094	37,460	15,257	22,203	168
06	184,013	58,923	32,351	26,572	141
07	263,476	98,645	49,998	48,647	156
08	118,740	66,008	26,273	39,735	164

Redistricting [Cleaning up]

# Create rook neighbors - ID adjacency
precinct_adjacency <- county_precincts_prj %>% 
  rownames_to_column(., var = "id") %>%
poly2nb(., row.names = "id", queen = F)
for(i in 1:729){
  precinct_adjacency[[i]] <- precinct_adjacency[[i]]-1
}

# Apportion block groups to precincts
precincts_demography <- cnty_bgs_prj %>%
  select(pop, CVAP, `White, Not_Latinx`:`Any race, Latinx`) %>%
  st_interpolate_aw(., 
                    county_precincts_prj,
                    extensive = T) %>%
  st_set_geometry(NULL)

# DATA FRAME FOR REDISTRICTING JP/CONSTABLE
precincts_JPConst_data <- county_precincts_prj %>%
  select(-CNTY, -Commish, -JP) %>%
  st_set_geometry(NULL) %>%
  merge(., precincts_demography) %>%
  mutate(PREC = as.numeric(PREC)) %>%
  left_join(constable_2020_all, by = c("PREC" = "precinct")) %>%
  left_join(partisanship, by = c("dnc_id" = "dnc_precinct_id")) %>%
  select(-PREC, -Group.1, -democrat:-republican) %>%
  mutate(dems = highd + lowd,
         repubs = total_reg_voters - dems)
precincts_JPConst_lst <- list(precincts_JPConst_data)

precincts_JP_Const_all <- list(precinct_adjacency, precincts_JPConst_data)
names(precincts_JP_Const_all) <- c("precinct_adjacency", "precinct_demogs")

  # alg_mcmc_JPConst <- redist.mcmc(adjobj = precincts_JP_Const_all$precinct_adjacency,
  #                        popvec = precincts_JP_Const_all$precinct_demogs$pop,
  #                        initcds = ,
  #                        ndists = 4,
  #                        nsims = 10000,
  #                        savename = "alg_mcmc_JPConst_redist")